Predictive Analytics on Student Academics

International Journal of Computer Trends and Technology (IJCTT)          
© 2016 by IJCTT Journal
Volume-34 Number-2
Year of Publication : 2016
Authors : Mr. K. Balaprasath, Mr. L. Arun Raj


Mr. K. Balaprasath, Mr. L. Arun Raj "Predictive Analytics on Student Academics". International Journal of Computer Trends and Technology (IJCTT) V34(2):93-97, April 2016. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Academic performances among university students are the topic of interest in educational society. The students performance plays a significant role for the course discontinuation. A large set of academic data is used for predicting the students yearly to fulfil the degree requirements. Two data processing algorithms have been used K-Means clustering and Apriori combined with Linear Regression are applied. The proposed system is to predict the learning concert of the learners supported both academic and non academic records. Data collected from the students via Google Forms are analyzed using the mining algorithms and the results are displayed using a visualization tool. Based on the analysis the academic performance of the student could be evaluated, thereby initiating steps to enhance the teaching learning process.

[1] Camilo Ernesto Lopez Guarín, Elizabeth León Guzman and Fabio A. Gonzalez, “A Model to Predict Low Academic Performance at a Specific Enrollment Using Data Mining”, IEEE Journal of Latin-American Learning Technologies, vol.10, no.3, pp.119-125, 2015.
[2] Shaymaa E.Sorour, Tsunenori Mine, Kazumasa Goda and Sachio Hi rokawa, “Comments Data Mining for Evaluating Students Performance”, International Conference on Advanced applied Informatics, pp.25-3, 2014.
[3] Nidyanandan Pratheesh and Devi Thiru pathi, “Sensation of Learning Analytics to Prevail the Software EngineeringEducation”, International Conference on Advanced Computing and Communication Systems, pp.1-7, 2013.
[4] Xin Li and Xuehui Zhang and Xin Liu, “A Visual Analytics Approach for E- learning Education”, International conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp.34-40, 2015.
[5] Ashkan Sharabiani, Fazle Karim, Anooshiravan Sharabiani, Mariya Atanasov and Houshang Darabi, Member, "An Enhanced Bayesian Network Model for Prediction of Students Academic Performance in Engineering Programs", Global Engineering Education Conference, pp.832- 837, 2014.
[6] Shaymaa E. Sorour, Jingyi Lu, Kazumasa Goda and Tsunenori Mine, "Correlation of Grade Prediction Performance with Characteristics of Lesson Subject", International Conference on Advanced Learning Technologies, pp.247-49, 2015.

Educational Data Mining (EDM), Kmeans Clustering, Apriori, Linear Regression.